Pathfinding gene circuits

The UB/COEGM Pathfinder Scholarship Program is a partnership between the University of Bridgeport School of Health Sciences and the Center of Excellence in Generative Medicine. Its mission is to groom developing physicians in the skills of twenty-first century precision medicine through the use of bioinformatics. It’s been my honor and privilege to act as a mentor for Pathfinder program for the past five years.

We’ve finally built a website for the program, which, in addition providing information on the program, also hosts biographical data on the Pathfinders, and will act as a code repository for their own explorations. Feel free to explore the site and get to known the students. Most have quite fascinating pre-Pathfinder lives and biographies.

Pathfinder scholars with old dude.

The Pathfinder program has taken on what appears to be a very significant challenge to modern medicine: Today’s physicians cannot easily process the increasingly available large amounts of data (such as produced by genome or microbiome reporting services) in any sort of real-time efficient manner. This is indeed a dilemma, as ‘big data’ approaches (in particular those employing machine learning algorithms) are increasingly pointing the way to a more precise, personalized type of treatment based on high-value conclusions.

In fact it’s been said that ‘more data is better than better data.’ But that is another blog.

On August 25 2018, the COEGM announced the release of ‘Circuits’ a gene-based open source platform combining genomic data in a variety of disparate dimensions. Circuits is web-based, has an imaginative and intuitive user interface, and is free to use. Pathfinders will continue to build new applications into Circuits, including tools to analyze the microbiome (microorganisms) and metabolome (small molecules).

Circuits employs a variety of popups to provide additional contextual data. For example, clicking on any agent linked to the expression of the target gene will trigger a popup window that draws a unique radar plot that we call the ‘genomic logo’ of the agent. This logo depicts the strength, action and targets of the indicated agent using a complex algorithm based on study design, scope and subject type.

Radar plot of Alpha-Linolenic acid

Most of the data used by Circuits was developed initially for use by the Opus23 application from publicly available repositories. Exceptions include the SNP and agent expression datasets, which were developed entirely by Datapunk human curators. A few of the larger sources are listed as references. [1-6]

Data connections featured in Circuits

Readers are encouraged to ‘surf’ Circuits and explore the target genes that seem more interesting. Click away! However here are a few hard links to can help get you started.

  1. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018 Jan 4. PubMed PMID: 29165669.
  2. Prasad, T. S. K. et al. (2009) Human Protein Reference Database – 2009 Update. Nucleic Acids Research. 37, D767-72.
  3. Liu YI1, Wise PH, Butte AJ. The “etiome”: identification and clustering of human disease etiological factors. BMC Bioinformatics. 2009 Feb 5;10 Suppl 2:S14. doi: 10.1186/1471-2105-10-S2-S14.
  4. A Kaplun, J D Hogan, F Schacherer, A P Peter, S Krishna, B R Braun, R Nambudiry, M G Nitu, R Mallelwar & A Albayrak PGMD: a comprehensive manually curated pharmacogenomic database. The Pharmacogenomics Journal volume 16, pages 124–128 (2016)
  5. Thul PJ1, Lindskog C2. The human protein atlas: A spatial map of the human proteome. Protein Sci. 2018 Jan;27(1):233-244. doi: 10.1002/pro.3307. Epub 2017 Oct 10.
  6. Albert-László Barabási, Natali Gulbahce, and Joseph Loscalzo. Network Medicine: A Network-based Approach to Human Disease. BMC Bioinformatics. 2009; 10(Suppl 2): S14.

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